Time-dependent series variance estimation via recurrent neural networks

Authors:
Nikolay Nikolaev;Peter Tino;Evgueni Smirnov
Affiliations:
Department of Computing, Goldsmiths College, University of London, London, United Kingdom;School of Computer Science, The University of Birmingham, Birmingham, United Kingdom;Department of Computing, MICC, IKAT, Maastricht University, Maastricht, The Netherlands
Venue:
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Year:
2011

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Abstract

This paper presents a nonlinear model for computing the time-dependent evolution of the variance in time series of returns on assets. First, we design a recurrent network representation of the variance, which extends the typically linear models. Second, we derive temporal training equations with which the network weights are inferred so as to maximize the likelihood of the data. Experimental results show that this dynamic recurrent network model yields results with improved statistical characteristics and economic performance.